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The visualization of complex network traffic involving a large number of communication devices is a common yet challenging task. Traditional layout methods create the network graph with overwhelming visual clutter, which hinders the network understanding and traffic analysis tasks. The existing graph simplification algorithms (e.g. community-based(More)
Ising mean field is a basic variational inference method for Ising model, which can provide an effective approximate solution for large-scale inference problem. The main idea is to transform a probabilistic inference problem into a functional extremum problem by variational calculus, and solve the functional extremum problem to obtain approximate marginal(More)